The traditional design approach of grabs and other bulk handling equipment consists of manufacturing and testing physical prototypes. A novel design approach is to use a co-simulation of MultiBody Dynamics (MBD) and Discrete Element Method (DEM), in which the virtual prototype of a new concept interacts with bulk solids. Therefore, this study develops and validates a full-scale co-simulation that models the grabbing process of cohesive and stress-history dependent iron ore. First, by executing in-situ measurements during the unloading of a vessel, grab-relevant bulk properties of the cargo, such as penetration resistance, are determined. Second, full-scale grabbing experiments are conducted in the cargo hold, which allows the process to be recorded in realistic operational conditions. Third, full-scale co-simulation is set up using the material model that has been calibrated based on an elasto-plastic adhesive contact model. Fourth, the co-simulation is validated by comparing its predictions to experimental data from various aspects, such as the force in cables and the torque in winches. The validated co-simulation proves that the stress-dependent behaviour of cohesive cargo as it interacts with the grab could be captured successfully. Valuable information such as a grab's kinematics and dynamics, as well as the porosity distribution of collected bulk solids, can be extracted from the simulation, supporting engineers to enhance the design and operation of equipment.

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The advances of mechatronic system design and system integration have shown improvements in functionality, performance and energy efficiency in many applications across industries, from autonomous ground vehicles and drones to conveyor belts. This trend has been adopted in some industries more than others. The design of equipment to handle granular or bulk material is commonly based on traditional approaches. Therefore, introducing mechatronic concepts in the design procedure can enable new possibilities, such as sensor integration and data analyses, adaptability and control. The efficiency of bulk material handling equipment in ports, agriculture and food processing is heavily influenced by the operational conditions. Typically, a piece of equipment is designed for defined operational conditions when the maximum performance can be achieved. In this work the concept of adaptability to varying operational conditions is explored by understanding the technologies implemented in other industries and the feasibility to be implemented in the bulk handling equipment design. Sensing technology, actuation and adaptability are systematically presented in this work to support the design process of the next generation of bulk handling equipment. This will pave the way for incorporating the technological trends in the design, such as: sustainability, “smartness”, Internet of Things, Industry 4.0, digital twin and machine learning. Adaptive mechatronic solutions will play a crucial role in generating and implementing innovative sustainable solutions for bulk handling equipment.@en

Due to the high demand of iron ore products in the steel industry, they have the largest share in dry bulk trading per year, above coal and grains. Approximately 9000 Cape-size bulk carriers with capacities up to 400 000 tonnes (DWT) transport the annual demand of iron ore to destination ports. Grabs are employed extensively to unload iron ore from ship holds. A fast and reliable unloading process is required to maintain a minimized cost for port operators and to deliver iron ore products to customers on time. In practice, many factors, such as moisture, varying material properties over the cargo depth and grab’s dynamics, contribute in creating challenges for achieving the desired performance during the unloading process. A solution for improving the unloading process is to enhance the design of grabs by using simulation-based methods. This enables a higher mass of iron ore to be collected per grab cycle, thus minimizing the total unloading time of a bulk carrier. Virtual prototyping of grabs is a novel simulation-based method that allows for evaluating the design performance in an affordable way. The virtual prototype of a grab as it interacts with bulk material are co-simulated at full-scale by coupling two different solvers: Discrete Element Method (DEM) and MultiBody Dynamics (MBD). The co-simulation requires virtual crane operator, CAD model of grab connected to a crane, and calibrated DEM material model as inputs. Over the past decade, reliable DEM calibration procedures have been developed to model free-flowing bulk solids, such as iron ore pellets, sand and gravel. However, due to moisture content the majority of iron ore products show cohesive and stress-history dependent behaviours, which should be considered in the calibration procedure. Additionally, considering particle size and shape of such fine iron ore products, the extreme computation time of DEM simulations is a challenge to be solved. Furthermore, a grab is often used to handle a broad variety of iron ore cargoes that are different in their properties, such as moisture content, shear strength and bulk density. The variability of bulk solid properties influences the grabbing process considerably, and thus, the grab’s efficiency. The primary objective of this dissertation is to develop an accurate co-simulation of grab and cohesive iron ore, and utilizing it for optimizing virtual prototypes. Once properties of an iron ore product in interaction with equipment are characterized, a reliable multi-variable calibration procedure needs to be employed to set various input parameters of a DEM material model, including continuous and categorical variables. Furthermore, once proper scaling rules are applied on the DEM simulation, a full scale grab-material co-simulation can be set up to be validated. Next, by determining the optimal settings of design variables the effect of bulk cargo variation on the grab’s efficiency can be minimized. This is the fundamental strategy of robust grab design. Bulk terminal operators value grabs that are optimized for multiple objectives, including a maximized efficiency with a minimized deviation. @en

This paper presents a multi-step DEM calibration procedure for cohesive solid materials, incorporating feasibility in finding a non-empty solution space and definiteness in capturing bulk responses independently of calibration targets. Our procedure follows four steps: (I) feasibility; (II) screening of DEM variables; (III) surrogate modeling-based optimization; and (IV) verification. Both types of input parameter, continuous (e.g. coefficient of static friction) and categorical (e.g. contact module), can be used in our calibration procedure. The cohesive and stress-history-dependent behavior of a moist iron ore sample is replicated using experimental data from four different laboratory tests, such as a ring shear test. This results in a high number of bulk responses (i.e. ≥ 4) as calibration targets in combination with a high number of significant DEM input variables (i.e. > 2) in the calibration procedure. Coefficient of static friction, surface energy, and particle shear modulus are found to be the most significant continuous variables for the simulated processes. The optimal DEM parameter set and its definiteness are verified using 20 different bulk response values. The multi-step optimization framework thus can be used to calibrate material models when both a high number of input variables (i.e. > 2) and a high number of calibration targets (i.e. ≥ 4) are involved.

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Ship unloader grabs are usually designed using the manufacturer's in-house knowledge based on a traditional physical prototyping approach. The grab performance depends greatly on the properties of the bulk material being handled. By considering the bulk cargo variability in the design process, the grab performance can be improved significantly. A multi-objective simulation-based optimization framework is therefore established to include bulk cargo variability in the design process of grabs. The primary objective is to reach a maximized and consistent performance in handling a variety of iron ore cargoes. First, a range of bulk materials is created by varying levels of cohesive forces and plasticity in the elasto-plastic adhesive DEM contact model. The sensitivity analysis of the grabbing process to the bulk variability allowed three classes of iron ore materials to be selected that have significant influence on the product performance. Second, 25 different grab designs are generated using a random sampling method, Latin Hypercube Design, to be assessed as to their handling of the three classes of iron ore materials. Of this range of grab designs, optimal solutions are found using surrogate modelling-based optimization and the NSGA-II genetic algorithm. The optimization outcome is verified by comparing predictions of the optimization algorithm and results of DEM-MBD co-simulation. The established optimization framework offers a straightforward and reliable tool for designing grabs and other similar equipment.

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The design of machinery for handling granular materials relies mainly on empirical methods and in-house engineering knowledge. This traditional approach provides incremental improvements that are often limited. Advancements in simulation and optimization can offer a promising alternative approach. Most of the research involved in improving or optimizing equipment design does not include the realistic performance of the new prototype and as such it is uncertain that the predicted performance is also guaranteed in practice. In this study, a design framework for a new generation of machinery handling granular materials, grabs, has been established that includes a full-scale validation step. This has been proven to lead to a breakthrough in equipment design. This design framework uses a co-simulation between Discrete Element Method (DEM) and Multi Body Dynamics (MBD), thus, capturing operational conditions in full-scale. The DEM simulation supported design step integrated as the main step to generate new prototypes. The performance of the prototype is evaluated by conducting full-scale experiments, thus validating the adequacy of the new design as well as the accuracy of the co-simulation. Through this a full design cycle has been fulfilled and a validated model has been achieved that is independent of specific design configurations.

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An ideal unit of bulk transport or storage equipment is able to handle cohesive iron ore with consistent productivity. In practice, however, uncontrollable bulk property variations affect the productivity. Therefore, it is important to understand the effect of uncontrollable variations on the process. This study quantifies variability and interdependency of bulk property of a range of cohesive iron ore products. Three different laboratory tests relevant to storage and excavation processes are used. Using a multi-variate experimental plan, three influencing characteristics of iron ore – type, moisture content and consolidation state – are included. A stress-history dependent behavior is captured in both the shear and penetration tests, with the results being highly dependent on the pre-consolidation stress. The outcome of this study enables future research on minimizing the effect of uncontrollable bulk properties variability of iron ore and other cohesive materials in the design procedure of transport and storage processes.

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This research demonstrates capturing different stress states and history dependency in a cohesive bulk material by DEM simulations. An automated calibration procedure, based on the Non-dominated Sorting Genetic Algorithm, is applied. It searches for the appropriate simulation parameters of an Elasto-Plastic Adhesive contact model such that its response is best fitted to the shear stress measured in experiments. Using this calibration procedure, the optimal set of DEM input parameters are successfully found to reproduce the measured shear stresses of the cohesive coal sample in two different pre-consolidation levels. The calibrated simulation resembles the stress history dependent values of shear stress, bulk density and wall friction. Through the case study of the ring shear tester, this research demonstrates the robustness and accuracy of the calibration framework using multi-objective optimization on multi-variable calibration problems irrespective of the chosen contact model.@en

The computation time of Discrete Element Method (DEM) simulations increases exponentially when particle size is reduced or the number of particles increased. This critical challenge limits the use of DEM simulation for industrial applications, such as powder flow in silos. Scaling techniques can offer a solution to reduce computation time. In this paper, we have developed a hybrid particle-geometric scaling approach with a focus on Elasto-Plastic Adhesive contact models. It established relationships between particle scaling factors and DEM contact input parameters. The isolated effects of varying particle size and geometric dimensions on bulk properties were also evaluated using uniaxial consolidation, static angle of repose, and ring shear tests. This paper shows how the particle scaling can be applied together with geometric scaling to incorporate two important aspects of bulk materials, their Elasto-Plastic behaviour and their cohesive forces.

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Purpose: The development of bulk material handling equipment can be accelerated and made less expensive when testing of virtual prototypes is adopted. However, the modelling of a grab unloader requires a large volume (77 m3) of iron ore pellets, making the computational costs prohibitive. This paper investigates the extent to which the original particles can be substituted by larger, coarser grains. It is crucial that this particle upscaling does not alter the realistic behaviour of the simulated bulk material, nor its interaction with the bulk handling equipment. Approach: First, our coarse graining technique is explained and set out for the particle system at hand. The material behaviour is then characterized using three laboratory experiments (two angle of repose tests and a penetration test). Next, the results of simulations using two contact models with and without coarse graining with different scale factors are compared with the measured material behaviour and material-equipment interaction. This includes a comparison of the macrobehaviour of the bulk material and the tool interaction of coarser grains in a cutting and sliding process. After reaching a satisfactory verified solution on the laboratory scale, the material behaviour and interaction behaviour of a large-scale experiment are modelled. A simulation model of a grab unloader was used for validation of the chosen coarse graining approach. Findings: Using the scaling method presented, the macroscopic tests indicated consistent material behaviour, regardless of the chosen particle scale for two contactmodels. Scaling of the tool interaction process produced mixed results: the sliding process scaled consistently but the penetration process did not, most likely because it is significantly harder for coarser grains to move since they have to move further to the sides before the tool can pass, leading to higher normal forces and frictional forces on the tip. This inconsistency was compensated for by adjusting the wall friction coefficient in the tip of the penetration tool. Once this adapted coarse graining scheme was applied to the industrial-scale simulation of a grab unloader, it produced consistent particle-scale invariant results. Originality/value: This research is the first to show how coarse graining schemes for DEM simulations can be applied to large-scale bulk handling equipment involving dominance of material equipment interaction through penetration of the bulk material.

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In general, input parameters of Discrete Element Method (DEM) simulations are calibrated by minimizing the difference of simulations output and laboratory measurements. To produce comparable bulk responses, in general, these (calibration) simulations replicate laboratory setup and procedures at a scale of 1:1. In terms of volume of bulk material, there is a considerable difference between laboratory experiments and large scale industrial applications, such as grab ship unloaders. For that reason, DEM simulations of large scale industrial applications often lead to an extreme computation time, especially for fine bulk materials. To reduce the computation time, scaling techniques can offer a solution. In this paper, a scaling method with the focus on an adhesive elasto-plastic DEM contact model is established. The scaling method is based on extending the coarse graining principles described in [1]. In the coarse graining, original DEM particles are substituted by larger grains. We establish the relationship between the scaling factor and the contact settings. Furthermore, the influence of the coarse graining on bulk properties in the quasi-static regime processes, such as the ring shear test, is investigated. The adequacy of the proposed scaling method is confirmed for both the simulated bulk materials as well as the interaction with the bulk handling equipment. Using the developed scaling method, virtual prototyping of bulk handling equipment and its interaction with cohesive materials can be done with a practical computational cost.@en

This paper describes the DEM simulation supported design approach leading to a breakthrough in grab design. More specifically it aims to demonstrate the full design cycle including evaluation of the grab prototype on full-scale in realistic operational conditions and comparison with the predictions by the DEM-MBD model. Most of the researches involved in improving or optimizing equipment design do not include the practical performance of the optimized design and as such it is hard to judge whether the predicted performance matches the obtained one in full-scale industrial practice. We show the successful prediction of the new design’s performance by comparing the DEM-MBD model to in-situ tests with the newly designed grab prototype. By this a true validation has been established and a validated model has been achieved that is independent of a specific grab design.@en

The performance of bulk solids digging equipment, such as grabs and bucket-wheel excavators, are highly dependent on their penetration process. Additionally, majority of bulk solids show cohesive behavior and consequently their penetration resistance is dependent on level of consolidation. Therefore, this paper aims to develop a reliable simulation using Discrete Element Method (DEM) that is able to quantitatively replicate the relationship between the penetration resistance and the pre-consolidation stress. Both laboratory experiments and simulations were performed in order to analyze differences in penetration under various pre-consolidation stress levels. The developed DEM simulation will be used for further improvements in the penetration process of bulk solids digging equipment.@en

Grabs are often used for unloading bulk carriers that deliver iron ore cargoes. The grabbing process starts with cutting the free surface of the bulk iron ore. For that reason, the initial penetration depth of the grabs' knives into the material is an important success factor in their filling ratio. The resistance to penetration is influenced by the consolidation process of the cargo which occurs during ship loading and sailing. In this study, a test method is developed to mimic the penetration process of the grabs' knives into material, and to determine the relationship between the level of consolidation stress and the penetration resistance. The test results show that when the consolidation stress increases, the penetration resistance, and the bulk density also increase. The results obtained from these measurements will be used in further research for improving the design of iron ore grabs.@en

In this research, DEM simulations are used to numerically replicate the behavior of cohesive coal in a ring shear tester. An automatic calibration procedure, based on the Non-dominated Sorting Genetic Algorithm, is applied to search for the appropriate simulation parameters such that its response is best fitted to the experimental macroscopic response. Using this calibration procedure, DEM input parameters are optimized successfully in reproducing the cohesive flowability of the coal sample. Through the case study of the ring shear tester, this research demonstrates the robustness and accuracy of the calibration framework using multi-objective optimization on multivariable calibration problems.@en

A test method to investigate the effect of compaction on the penetration resistance of iron ore fine. Grabs are often used in dry bulk terminals for unloading bulk carriers loaded with iron ore. The grabbing process starts with cutting the free surface of the bulk iron ore. The penetration depth of the grab knives into bulk material is an important success factor in the filling ratio of the grab. The resistance to penetration is influenced by the densification process of cargo which occurs during ship loading and during sailing. In this study a test method is developed to determine the relationship between the level of compaction stress and the measured penetration resistance on the penetration tool. The test results show that when the compaction stress increases, the penetration resistance, as well as the bulk density also increases. The results obtained from these measurements will be used in further research for improving the design of iron ore grabs. @en